How are mujoco environments used for meta-rl?
According to this paper (PEARL):

These locomotion task families require adaptation across reward
functions (walking direction for Half-CheetahFwd-Back, Ant-Fwd-Back,
Humanoid-Direc-2D, target velocity for Half-Cheetah-Vel, and goal
location for Ant-Goal2D) or across dynamics (random system parameters
for Walker-2D-Params).

It looks like different versions of the same environment with differing reward functions are used. For eg., Forward direction might be rewarded positively in one version, Negative in another, Both directions rewarded positively in yet another.